Mission and Scope
To define a data capture architecture consisting of immutable schema bases and interoperable overlays for Internet-scale deployment.
Conveners
- Paul Knowles (Human Colossus Foundation)
- Robert Mitwicki (Human Colossus Foundation)
Interested Members (add your name if you may be interested in joining this proposed WG)
- Philippe Page
- Haydar Majeed
- Rieks Joosten
- Lucy Yang
- Sal D’Agostino
- Zan McNaught
- Mike Bennett
- Colin Wallis
- John Walker
- Mark Lizar
- Burak Serdar
- Scott Whitmire
- Dennis Landi
- Darrell O’Donnell
- Kalyan Kulkarni
- Zeljko Milinovic
- Crt Ahlin
Description
Decentralized Semantics
The post millennial generation has witnessed an explosion of captured data points which has sparked profound possibilities in both Artificial Intelligence (AI) and Internet of Things (IoT) solutions. This has spawned the collective realization that society’s current technological infrastructure is simply not equipped to fully support de-identification or to entice corporations to break down internal data silos, streamline data harmonization processes and ultimately resolve worldwide data duplication and storage resource issues. Developing and deploying the right data capture architecture will improve the quality of externally pooled data for future AI and IoT solutions.
Overlays Capture Architecture (OCA)
OCA is an architecture that presents a schema as a multi-dimensional object consisting of a stable schema base and interoperable overlays. Overlays are task-oriented linked data objects that provide additional extensions, coloration, and functionality to the schema base. This degree of object separation enables issuers to make custom edits to the overlays rather than to the schema base itself. In other words, multiple parties can interact with and contribute to the schema structure without having to change the schema base definition. With schema base definitions remaining stable and in their purest form, a common immutable base object is maintained throughout the capture process which enables data standardization.
OCA facilitates a unified data language so that harmonized data can be pooled for improved data science, statistics, analytics and other meaningful services.